Systems and methods for proactive network diagnosis

ABSTRACT

Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for proactive network diagnosis. An example method may include determining, by one or more processors, telemetry data and streaming trap data indicative of a group of cable modem devices being disconnected from a cable network. The example method may include determining, based on the telemetry data and streaming trap data, a first network node device of the group of network node devices. The example method may include generating first performance data associated with the first network node device. The example method may include determining, based on a comparison between the first performance data and an event criterion, an occurrence of an event associated with the first network node device.

TECHNICAL FIELD

The disclosure generally relates to proactive network diagnosis. In someembodiments, the disclosure may more specifically relate to proactivenetwork diagnosis on Data Over Cable Service Interface Specification(DOCSIS) networks.

BACKGROUND

A wide variety of network service providers may establish communicationnetworks to connect customer-premises equipment or customer-providedequipment (CPE) to one or more networks such as DOCSIS networks.Traditionally deploying and maintaining such communication networks mayrequire reliable communication between components of a network, whichmay be costly to deploy and maintain. In some instances, networks (e.g.,DOCSIS networks) may experience impairments, outages, and/orintermittent disconnection issues, which may result in reduced userexperience. Typically such network issues may be identified after theissues have occurred by users (e.g., customers) informing (e.g., bycalling, emailing, or the like) the network service providers about theissues. The network service providers may dispatch technicians to alocal location where an issue has occurred to determine whether theissue is originated from services with the network or with an individualCPE. Therefore, the conventional system and methods are time-consumingand are incapable of identifying network issues timely, efficiently, andaccurately.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingdrawings. The use of the same reference numerals may indicate similar oridentical items. Various embodiments may utilize elements and/orcomponents other than those illustrated in the drawings, and someelements and/or components may not be present in various embodiments.Elements and/or components in the figures are not necessarily drawn toscale. Throughout this disclosure, depending on the context, singularand plural terminology may be used interchangeably.

FIG. 1 depicts an illustrative system for proactive network diagnosis ina cable network, in accordance with example embodiments of thedisclosure.

FIGS. 2A-2B depict an example device polling process, in accordance withone or more example embodiments of the disclosure.

FIG. 3 depicts an example flow diagram showing a method for proactivenetwork diagnosis, in accordance with one or more example embodiments ofthe present disclosure.

FIG. 4 depicts an example network architecture, in accordance with oneor more example embodiments of the disclosure.

FIG. 5 depicts an example network architecture, in accordance with oneor more example embodiments of the disclosure.

FIG. 6 depicts an example computing entity, in accordance with one ormore example embodiments of the disclosure.

DETAILED DESCRIPTION

Illustrative embodiments will now be described more fully hereinafterwith reference to the accompanying drawings, in which some, but not allembodiments are shown. The disclosure may be embodied in many differentforms and should not be construed as limited to the embodiments setforth herein; rather, these embodiments are provided so that thisdisclosure will satisfy applicable legal requirements. Like numbersrefer to like elements throughout.

Example aspects of the present disclosure are directed to methods andsystems for proactive network diagnosis. A sustained offline conditionmay describe a state that a device is completely off (e.g., a nodeoutage, or the like). The sustained offline condition may also be achronic issue initially manifesting intermittent network issues, such asa node may intermittently go offline and recover multiple times over acourse of a given time period before the node is offline for a sustainedperiod, or signal degradations may result in increasingly frequentservice interruptions specific to the services being utilized by thecustomer. Typically, the intermittent network issues may not be isolatedand understood for determining whether or not a sustained offlinecondition will occur until the sustained offline condition has occurred.Proactive network diagnosis may diagnose and identify issues (e.g., nodeoutage and/or other sustained offline condition issues) within a DOCSISnetwork before the sustained offline condition has occurred and/orbefore users (e.g., customers) report the issues by analyzingcomprehensive data associated with one or more network componentsassociated with a given node (e.g. line extenders, amplifiers, taps,etc.) to predict whether or not the sustained offline condition willoccur, which may provide improved service impairment or sustainedoffline condition detection early, timely, efficiently, and accurately.

For instance, an intermittent network issue may occur during a randomtime period (e.g., every day at 3 am or 3 pm) and may last for a certaintime period (e.g., one or more hours), and then the issues may be gone.Conventionally, a user may report the intermittent network issue (e.g.,by contacting a network service provider). A technician may bedispatched to a location where the intermittent network issue hasoccurred. However, the technician may not find the root cause of theissue or even confirm that there is an issue as the underlying cause mayhave subsided by the time the technician arrives at the location. Afterthe technician leaves, the issue may reoccur. The conventional methodsmay require users to report issues after the issues have occurred andmay also require technicians to physically go to the location toidentify whether the issues originate from problems in the network orwithin an individual customer's premise (e.g. tap, drop, demark, insidewiring, etc.) after the technician visits the location multiple times toisolate the root cause of the issues. However, some users may keepsilent about intermittent network issues, and those users may not reportany issues until a network service and/or device experiences a sustainedoffline condition. Thus, the conventional methods may be incapable ofidentifying issues in a timely or efficient manner before users reportthe issues and/or before the technician physically goes to locations toisolate root cause.

According to example aspects of the present disclosure, the systems andmethods of the present disclosure may aggregate comprehensive dataassociated with a DOCSIS network and utilize the aggregated data toautomatically determine an occurrence of an event (e.g., a sustainedoffline condition for a node will occur, an intermittent network issuehas occurred for a node, an intermittent network issue has beenoccurring for a node, or the like). For example, a system may comparethe comprehensive data with one or more control thresholds. A controlthreshold may describe a value or a value range for determining anoccurrence of an event. If the comprehensive data deviates from one ormore control thresholds (e.g., exceeding one or more controlthresholds), the system may determine an occurrence of an event. If thecomprehensive data does not deviate from one or more control thresholds,the system may determine that the event is not occurring, has notoccurred, or will not occur. For example, if the comprehensive datadeviates from a first group of control thresholds, the system maydetermine that intermittent network issues have been occurring or haveoccurred. If the comprehensive data further deviates from a second groupof control thresholds, the system may determine that a sustained offlinecondition has occurred. If the comprehensive data does not deviate fromthe first group of control thresholds, the system may determine thatintermittent network issues have not occurred. If the comprehensive datadoes not deviate from the second group of control thresholds, the systemmay determine that the sustained offline condition will not occur.

The comprehensive data may include device data (e.g., identificationdata, performance data, or the like) associated with a cable modemtermination system (CMTS), one or more network node devices, one or moretaps, one or more CPEs, one or more cable modem (CM) devices, and/or anyrelevant devices within a DOCSIS network, streaming data that maydetermine bandwidth allocation or the amount of data a user consumes,telemetry data obtained from an intelligent polling process (thisintelligent polling may be described in more detail in the processoutlined in FIGS. 2A-2B), and/or any relevant data associated with theDOCSIS network. Identification data may include a MAC address, a devicestatus, a device DOCSIS profile, a channel frequency, a modulationprofile, a modulation type, a channel bandwidth, and/or any relevantdata for identifying a device. Performance data may include forwarderror correction information, upstream/downstream data transfer rates,signal to noise ratios (SNRs), modulation error ratios, measurement datato characterize one or more channels associated with signals outputtedby CPEs (e.g., slopes measurement data, amplitude measurement data,ripple measurement data, spike (for example, non-linear noisedistortions) measurement data, absolute received power per subcarriermeasurement data, error vector magnitude measurement data, and thelike), device power levels, CMTS power levels, a CMTS equalizationcoefficient, a device equalization coefficient, and/or any relevant dataassociated with a performance of the above devices. The CMTSequalization coefficient and device equalization coefficient may relateto the pre-equalization of devices within the DOCSIS network.Pre-equalization may be a DOCSIS feature that may improve upstreamperformance in the presence of network impairments. At a high level,pre-equalization is performed as follows. To begin, a first device (forexample, a CMTS) analyzes messages coming from a second device (forexample, a cable modem) and evaluates the signal quality of themessages. If the first device determines that the messages can beimproved by pre-equalization, the first device sends equalizeradjustment values to the second device. The second device applies theseequalizer adjustment values, called coefficients, to its pre-equalizer.The result is that the second device may then transmit a pre-distortedsignal to compensate for impairments between the second device and thefirst device. As the pre-distorted signal traverses the network it willexperience the effects of radio frequency (RF) impairments. By the timethe pre-distorted signal from the second device arrives at the firstdevice, it will no longer have any of the original pre-distortion, asthe RF impairments will have transformed it back into a near-idealsignal at the first device. If further adjustments are required, thefirst device may send more pre-equalizer coefficient values to thesecond device and the cycle may repeat. With this in mind, and in someinstances, the CMTS equalization coefficient may be an equalizationcoefficient used by a CMTS for data transfer from the CMTS to anotherdevice, and the device equalization coefficient may be an equalizationcoefficient used by a device.

In some embodiments, the system may determine the comprehensive data viaone or more polling processes. For example, CMTS may send a poll requestto the CPE device and the CPE device may send telemetry data to theCMTS. In some embodiments, the CMTS may send the poll request insubstantially real time or real time or at a predefined time interval(e.g., 15-minute interval, or any relevant time interval for the pollingprocess), which may provide an efficient way to monitor one or more CPEstatuses. CPE statuses may then be aggregated in association with agiven tap (e.g., a tap may be a device that connects individuallocations to a cable network, such as a hybrid fiber coaxial network). Asimilar polling process may be performed for other devices (e.g., CPEdevices, node devices, or any relevant devices in a network). In someembodiments, if there is a change in a state (e.g., on/off state) of aCPE device (e.g., CM device, or the like), the CPE device may betriggered to generate streaming trap data indicative of the change. Atrap of the cable network (e.g., an SNMP (Simple Network ManagementProtocol) trap) may be a frequently used alert message sent from aremote device (e.g. a cable modem or set top box) to a central collector(e.g. a CMTS) that is the SNMP manager. Streaming trap data mayrepresent a constant streaming source of data that can be collected andaggregated to isolate lowest common failure points on the cable network(e.g., the hybrid fiber coaxial network). For example, streaming trapdata may be captured by the CMTS using SNMP communications.

In some embodiments, the system may attribute one or more offline trapsthat have been detected representing intermittent network issues (e.g.,intermittently off) and/or sustained offline conditions (e.g.,completely off) to a specific node based on the comprehensive data. Forexample, the system will track on-off status for CPE devices collectedvia SNMP traps, representing intermittent or sustained offline eventsisolated to a specific portion of the hybrid fiber coaxial networktopology. Impairments are isolated to the lowest common component of thenetwork by comparing offline or intermittently offline devices toengineering diagrams representing the latitude and longitude coordinatesof passive and active network devices. These lowest common componentsmay be passive taps, line extenders, amplifiers or nodes. The system maycombine this trap data with polling data to identify patterns ofdegradation that are evidence of imminent network failures, isolatingthe source of these failures to individual or multiple networkcomponents. The system may then inform users of imminent or currentintermittent impairments or sustained offline conditions, and may alsodispatch technicians to fix issues that may result in the outage.

In some embodiments, the system may determine that a given CPE has notbeen online for a sustained time period. In such instances, the devicerepresents either a disconnected customer or has been powered down. Inorder to prevent a false-positive outage event, such devices are removedfrom a list of active CPEs. For example, the system may check the statusof a CPE device on a recurring and/or daily basis. If the CPE device hasnot been on the network for a predefined time period (e.g., greater thanthree days, or the like), the system may remove the CPE from a list ofactive devices, thereby preventing a false-positive outage event.

In some embodiments, the system may determine whether intermittentnetwork issues have occurred for the specific node and/or whether asustained node outage will occur. For example, based on the above nodeattribution, the system may determine data (e.g., device data, streamingdata, and telemetry data) associated with the node. The system maycompare the data with one or more control thresholds. If the datadeviates from the one or more control thresholds (e.g., exceeding theone or more control thresholds), the system may determine that the nodehas been experiencing or has s experienced intermittent network issuesand/or a sustained node outage will occur. If the data does not deviatefrom the one or more control thresholds, the system may determine thatintermittent network issues have not occurred and/or a sustained nodeoutage will not occur. A sustained outage may describe a situation wherea node and all CPE devices serviced by that node may be completelyoffline or may be offline for a certain time period (e.g., 10 to 15minutes) and then the node may be restored to an online state, and wherethat issue may reoccurring an intermittent fashion, which may ultimatelyresult in the sustained node outage with the node being completelyoffline for a sustained and non-intermittent time period. In someembodiments, the system may determine a pattern of degradationassociated with performance data (e.g., upstream transmitting andreceiving levels, downstream transmitting and receiving levels, upstreamand downstream signal to noise ratios, the above-described performancedata associated with the node, or any relevant data to a performance ofthe node) and/or streaming data that will ultimately lead to theintermittent network issues and/or a sustained node outage. A patternmay indicate a distribution (e.g., a statistical distribution, or thelike) of the various types of performance data. For example, the systemmay measure various types of performance data and generate a patternindicative of distribution (e.g., a statistical distribution, or thelike) of the various types of performance data. The system may determinea pattern based on current performance data associated with the node andcompare the pattern with a historical pattern including historical dataassociated with the intermittent network issues and the sustained nodeoutage. The historical data may include historical performance data,user behavior data associated with the node (e.g., reporting issues in ageographical region associated with the node), and any relevant dataassociated with the node. User (e.g., service subscriber) behavior datamay include a call log indicative of whether or not users associatedwith a given node have called a service provider to report intermittentnetwork issues and/or a sustained node outage.

In some embodiments, the system may determine the pattern indicative ofa change of performance data over a time period. For example, the systemmay measure particular performance data (e.g., signal to noise ratio, orany relevant performance data) over a time period and the system maygenerate a pattern indicative of a function of the performance dataversus time (e.g., via plotting or any statistical methods).Additionally, and/or alternatively, the system may determine a patternindicative of a distribution of CPEs (e.g., CPEs having intermittentlyoff and/or completely off) associated with a specific node.

In some embodiments, the system may generate a machine learning model.The system may train the machine learning model using historical data(e.g., known patterns, historical performance data) and associatedintermittent network issues and sustained node outage. The system mayinput a detected pattern or measured performance data into the trainedmachine learning model and the trained machine learning model may outputa likelihood indicative of how probable an event is to occur (e.g., howlikely the intermittent network issues will occur and/or how likely asustained node outage will occur). In some embodiments, the system mayuse the historical data to train a machine learning model to classify anode into different classifications (e.g., a classification indicativeof nodes having been experiencing intermittent network issues, aclassification indicative of nodes having experienced intermittentnetwork issues, a classification indicative of nodes will experience asustained node outage, a classification indicative of a bad node that iscompletely off, a classification indicative of a healthy node, or thelike). The system may input a detected pattern or measured performancedata into the trained machine learning model and the trained machinelearning model may output a likelihood indicative of a classificationassociated with a specific node.

For instance, a user (e.g., service subscriber) may start up an oldtelevision at 3:00 p.m. on every Saturday, which may bring down anentire node because the old television may introduce signal noise oringress into the network through a cracked coaxial cable, introducing asustained offline event. The system may determine that a node has beenexperiencing and/or experienced intermittent network issues periodicallybased on performance data associated with the node. The system maydetermine a pattern using the performance data and determine that asustained node outage will occur based on the pattern. The system maydispatch technicians to the location where intermittent network issueshave occurred to fix the issues.

In some embodiments, the system may take actions based on where theintermittent network issues have occurred for a specific node or asustained node outage may occur. For example, the system may allocateresources based on data associated with the specific node and maydispatch a technician to locations associated with the specific node. Inthis way, resources (e.g., a reduced number of trucks, technicians, orthe like) may be allocated more efficiently. In some embodiments, thesystem may send information associated with the issues and the node insubstantially real-time or real-time to technicians to facilitate theprocess of technicians fixing associated issues such that thetechnicians may have an ability to actively check levels in the network,leveraging their signal meters. For example, the system may send a pointof failure for the issues to technicians. As another example, the systemmay generate a graphical representation of a network topology overlaidwith the points of failure to expedite isolation of the impairment to acomponent failure indicative of the root cause of the intermittent orsustained network outage or impairment

Particular embodiments of the subject matter described herein can beimplemented to realize one or more of the following advantages, thesystem does not require a user to report issues, but the system mayprovide a timely and efficient way by automatically and dynamicallydetermining whether or not the issues have occurred or specifying theprobability that issues will occur. The system may not only use dataassociated with a node but also use comprehensive data associated withvarious devices that are connected to the node, which may determinewhether or not the node has and/or will have issues with heightenedaccuracy. In addition, the system may predict whether or not a sustainedoffline issue (e.g., a sustained node outage) will occur, which providesearly detection of the imminent sustained offline condition prior tooccurrence of these issues such that the service provider may resolvethe root cause before customers' services are impaired or the networkexperiences a sustained offline event. The system may also allocatehuman resources (e.g., field technicians) more efficiently based on theidentified and isolated issues, thereby, reducing the costs associatedwith deploying and maintaining networks and in turn increase profitmargin.

Other embodiments of this aspect include corresponding systems,apparatuses, and computer programs configured to perform the actions ofthe methods encoded on computer storage devices.

The details of one or more embodiments of the subject matter describedherein are outlined in the accompanying drawings and the descriptionbelow. Other features, aspects, and advantages of the subject matterwill become apparent from the description, the drawings, and the claims.

Various embodiments of the present disclosure now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments are shown. Indeed, the disclosure may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. The term “or” is used herein in both the alternative andconjunctive sense, unless otherwise indicated. The terms “illustrative”and “example” are used to be examples with no indication of qualitylevel. Like numbers refer to like elements throughout. Arrows in each ofthe figures depict bi-directional data flow and/or bi-directional dataflow capabilities.

Embodiments of the present disclosure may be implemented in variousways, including computer program products that comprise articles ofmanufacture. A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media includes all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid-state drive (SSD)), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, cloud-based storage (e.g. an objectstorage or database through a web service interface, or any othercloud-based storage) and/or the like. A non-volatile computer-readablestorage medium may also include a punch card, paper tape, optical marksheet (or any other physical medium with patterns of holes or otheroptically recognizable indicia), compact disc read only memory (CD-ROM),compact disc-rewritable (CD-RW), digital versatile disc (DVD), Blu-raydisc (BD), any other non-transitory optical medium, and/or the like.Such a non-volatile computer-readable storage medium may also includeread-only memory (ROM), programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), flash memory (e.g., Serial,NAND, NOR, and/or the like), multimedia memory cards (MMC), securedigital (SD) memory cards, SmartMedia cards, CompactFlash (CF) cards,Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), double datarate synchronous dynamic random access memory (DDR SDRAM), double datarate type two synchronous dynamic random access memory (DDR2 SDRAM),double data rate type three synchronous dynamic random access memory(DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DIMM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present disclosuremay also be implemented as methods, apparatuses, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present disclosure may take the form of an apparatus, system,computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present disclosuremay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisesa combination of computer program products and hardware performingcertain steps or operations.

Embodiments of the present disclosure are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatuses, systems, computingdevices, computing entities, and/or the like carrying out instructions,operations, steps, and similar words used interchangeably (e.g., theexecutable instructions, instructions for execution, program code,and/or the like) on a computer-readable storage medium for execution.For example, retrieval, loading, and execution of code may be performedsequentially such that one instruction is retrieved, loaded, andexecuted at a time. In some example embodiments, retrieval, loading,and/or execution may be performed in parallel such that multipleinstructions are retrieved, loaded, and/or executed together. Thus, suchembodiments can produce specifically-configured machines performing thesteps or operations specified in the block diagrams and flowchartillustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

FIG. 1 depicts an illustrative system 100 for proactive networkdiagnosis in a cable network, in accordance with example embodiments ofthe disclosure. In some aspects, the cable network of the system 100described herein may be implemented using a DOCSIS specification orequivalent specification. In some embodiments, the cable network mayinclude a fiber optic network that may extend from a headend out to aneighborhood's hub site, and finally to a coaxial cable node whichserves customers at several households, for example, 25 to 2000households. As shown in FIG. 1, a headend device 104 is connected to afirst network node device 108 and a second network device 120. The firstnetwork node device 108 connects DOCSIS CPEs (e.g., Cable Modem devices,Set top Box devices, embedded Multi-Media Terminal Adapter devices) 116a and 116 b at households 114 a and 114 b via amplifiers 110 a and 110 band taps 112 a and 112 b. The first network node device 120 connectsCPEs 128 a and 128 b at households 126 a and 126 b via amplifiers 122 aand 122 b and taps 124 a and 124 b. The headend device 104, a remotecomputing server 130, and a service server 140 communicate with eachother via a network 150.

The headend device 104 may include a CMTS. The headend device 104 mayinclude a converged cable access platform (CCAP) device. CCAP may referto equipment that combines aspects of the functionality of edgequadrature amplitude modulation (QAM) technology with CMTSs to provideservices such as internet and voice over the Internet Protocol (IP)while encoding and transmitting digital video channels over the cablenetwork. The headend device 104 may be configured to provide adownstream signal (e.g., a downstream broadband signal) to the CPEs 116a, 116 b, 128 a, and 128 b, and receive upstream signals from the CPEs116 a, 116 b, 128 a, and 128 b. For example, the headend device 104 maybe configured (i) to receive the downstream signal for a source (notshown in FIG. 1) that may electronically connect the headend device 104,(ii) convert the downstream signal into a radio frequency downstreamsignal, (iii) output the downstream signal onto the first and secondnetwork node devices 108 and 120 for communication to the CPEs 116 a,116 b, 128 a, and 128 b, (iv) receive the upstream signals from the CPEs116 a, 116 b, 128 a, and 128 b via the first and second network nodedevices 108 and 120, and (v) convert the received upstream signals intolight signals for communication to the headend device 104. In someembodiments (not shown in FIG. 1), the headend device 104 may include aswitch, for example, a network switch such as an Ethernet switch.

A network node device (e.g., the first network node device 108 or thesecond network node device 120) may be a device capable of creating,receiving, or transmitting information over a communications channel ina cable network as either a redistribution point or a communicationendpoint. The network node device such as remote physical (PHY) device,remote medium access control (MAC) device, and/or remote hybrid PHY/MACdevice. The remote PHY device may be a device having PHY layerfunctionality (that is, PHY layer functionality as described inconnection with the open systems interconnection model, OSI model. Asshown in FIG. 1, the first and second network node devices 108 and 120are external to the headend device 104. The first and second networknode devices 108 and 120 may be electronically connected to the headenddevice 104 via a cable (e.g., a fiber optic cable). In some embodiments(not shown in FIG. 1), the first and second network node devices 120 maybe included in the headend device 104. As shown in FIG. 1, the cablenetwork of the system 100 shows one of embodiments having two nodesplits corresponding to each of the first and second network nodedevices. In some embodiments (not shown in FIG. 1), the system 100 mayinclude a cable network having X node splits indicating X number ofgroups are connected to a given node. For instance, the first networkdevice 108 may connect a single household having one or more CPEs. Thesecond network device 120 may connect to more than two households, eachhaving one or more CPEs. Examples of X node splits are further describedin FIG. 7. In some embodiments (not shown in FIG. 1), the first networknode device 108 or the second network node device 120 may include aswitch, for example, a network switch such as an Ethernet switch.

The amplifier(s) 110 a, 110 b, 122 a, and 112 b may amplify signal. Forexample, the amplifier(s) 110 a, 110 b, 122 a, and 112 b may increasethe amplitude of the signal. In certain embodiments, the variouscomponents of a signal (e.g., low return path, forward path, high returnpath) may be amplified by respective amplification components of theamplifier(s) 110 a, 110 b, 122 a and 112 b. Each amplified signal maythen be output onto or driven back onto the cable line in a directionfor the signal. Any number of diodes or other suitable devices may beincorporated into the amplifier(s) 110 a, 110 b, 122 a, and 112 b inorder to prevent or limit undesired leakage of an amplified signal in adirection from which the signal was received. For example, theamplifier(s) 110 a and 110 b may receive a return path signal from thehouseholds 114 a and 114 b via the taps 112 a and 112 b, theamplifier(s) 110 a and 110 b may amplify the signal, and theamplifier(s) 110 a and 110 b may output the signal in an upstreamdirection towards the headend device 104 while limiting the output orleakage of the signal in a downstream direction. The amplifier(s) 122 aand 122 b may have similar structure and perform similar functions tothe amplifier(s) 110 a and 110 b.

In some embodiments, the amplifier(s) 110 a, 110 b, 122 a, and 112 b mayinclude a wide variety of gains that may be utilized for differentcomponents of a signal. In certain embodiments, the amplifier(s) 110 a,110 b, 122 a, and 112 b may be powered by a received signal, such as areceived downstream signal. Additionally or alternatively, theamplifier(s) 110 a, 110 b, 122 a, and 112 b may be powered by one ormore batteries and/or external power sources. In certain embodiments,the power requirements of the amplifier(s) 110 a, 110 b, 122 a, and 112b may be based at least in part on the modulation technique(s) utilizedin association with the signals that are amplified. In one exampleembodiment, a relatively low power amplifier(s) 110 a, 110 b, 122 a, and112 b may be provided in association with an OFDMA modulation technique.In some embodiments, a variable number of amplifiers may be locatedbetween a network node device (e.g., the first network node device 108or the second network node device 120) and a tap (e.g., the tap 112 a,112 b, 124 a, or 124 b).

A tap (e.g., the tap 112 a, 112 b, 124 a, or 124 b) may be a hardwaredevice (e.g., a passive device) that connects a number of householdseach having one or more CPEs. For example, as shown in FIG. 1, a singletap connects a single household. In some embodiments (not shown in FIG.1), a single tap may connects more than one households each having oneor more CPEs.

Any number of modulation techniques and/or data standards may beutilized by the cable network of the system 100. For example, televisiondata may be modulated utilizing a suitable quadrature amplitudemodulation (QAM) or other modulation technique, and the modulated datamay be incorporated into data signal (e.g., broadband data signal). Asanother example, an orthogonal frequency-division multiple access(OFDMA) technique, a time division multiple access (TDMA) technique, anadvanced time division multiple access (ATDMA) technique, a synchronouscode division multiple access (SCDMA) technique, or another suitablemodulation technique or scheme may be utilized to modulate data includedwithin the data signal. The data signal may be configured to provide awide variety of services to one or more households, including but notlimited to, television service, telephone service, Internet service,home monitoring service, security service, etc.

A CPE (e.g., the CPE 116 a, 116 b, 128 a, or 128 b) may be any terminaland associated equipment located at a subscriber's premises andconnected with a carrier's telecommunication circuit at a pointestablished in a building or complex to separate customer equipment fromthe equipment located in either the distribution infrastructure orcentral office of the communications service provider. Examples of a CPEmay include CM devices, telephones, routers, network switches,residential gateways (RG), set-top boxes, fixed-mobile convergenceproducts, home networking adapters, and Internet access gateways thatenable consumers to access providers' communication services anddistribute them in a residence or enterprise with a local area network(LAN). In some embodiments, a CPE may be an active equipment, as theones mentioned above, or passive equipment such as analog telephoneadapters (ATA), or xDSL-splitters (e.g., xDSL refers to the sum total ofdigital subscriber line (DSL) technologies). The CPEs may provideupstream signals to the headend device 104 via a respective tap,respective amplifiers, and a respective network node device.

The remote computing server 130 may communicate with the headend device104 and the service server 140 using one or more communication networks150. The remote computing server 130 may be one or more remotecloud-based on computers/servers, and/or network-based oncomputers/servers. The remote computing server 130 may include aproactive network diagnosis module 132, and one or more relevant modules(not shown in FIG. 1, e.g., communications modules configured tocommunicate with components inside and outside the remote computingserver 130).

In one or more embodiments, the one or more communications networks 150may include, but not limited to, any one of a combination of differenttypes of suitable communications networks such as, for example,broadcasting networks, cable networks, public networks (e.g., theInternet), private networks, wireless networks, cellular networks, orany other suitable private and/or public networks. Further, any of theone or more communications networks 150 may have any suitablecommunication range associated therewith and may include, for example,global networks (e.g., the Internet), metropolitan area networks (MANs),wide area networks (WANs), local area networks (LANs), or personal areanetworks (PANs). In addition, any of the one or more communicationsnetworks may include any type of medium over which network traffic maybe carried including, but not limited to, coaxial cable, twisted-pairwire, optical fiber, a hybrid fiber coaxial (HFC) medium, microwaveterrestrial transceivers, radio frequency communication mediums, whitespace communication mediums, ultra-high frequency communication mediums,satellite communication mediums, or any combination thereof.

The proactive network diagnosis module 132 may include at least oneprocessor and at least one memory storing computer-executableinstructions, that when executed by the at least one processor, causethe at least one processor to aggregate comprehensive data obtained fromthe headend device 104 via a polling process described in FIGS. 2A and2B and trap data associated with the CPEs 116 a, 116 b, 128 a, and 128b, and to utilize the aggregated data to automatically determine anoccurrence of an event (e.g., a sustained node outage, a node has beenexperiencing an intermittent network issue, and/or a node havingexperienced an intermittent network issue). As shown in FIG. 1, theproactive network diagnosis module 132 may aggregate comprehensive dataobtained from the headend device 104 and trap data associated with theCPEs 116 a, 116 b, 128 a, and 128 b. The proactive network diagnosismodule 132 may attribute one or more CPEs have been experiencing orexperienced intermittent network issues (e.g., a CPE has an off statusfor a short time period, and then the CPE is back to an on-status)and/or D (e.g., a CPE has an-off status for a sustained time period) toa specific node based on the comprehensive data. For example, based onthe polling process described in FIGS. 2A and 2B and trap data, theproactive network diagnosis module 132 may track on-off status for theCPEs 116 a, 116 b, 128 a, and 128 b. The proactive network diagnosismodule 132 may capture data associated with the CPEs havingintermittently off and/or completely off issues and then may associatesuch CPEs having the same node such that the proactive network diagnosismodule 132 may determine whether or not an associated node that isrepresented by all those different devices is mostly like to haveintermittent network and/or sustained offline conditions. As shown inFIG. 1, a dashed cross may indicate that the CPE device 116 b or 128 bhas an intermittently off issue. A solid cross may indicate that the CPEdevice 128 a has a sustained off condition. The proactive networkdiagnosis module 132 may attribute data associated with the CPE device116 b to the first network node device 108. The proactive networkdiagnosis module 132 may attribute data associated with the CPEs 128 aand 128 b to the second network node device 120.

In some embodiments, for each of the first network node device 108 andthe network node device 120, the proactive network diagnosis module 132may determine the occurrence of the event by comparing respective dataassociated with the first network node device 108 and the network nodedevice 120 with one or more control thresholds. Additionally, and/oralternatively, the proactive network diagnosis module 132 may determinethe occurrence of the event based on a machine learning model. For thefirst network node device 108, the proactive network diagnosis module132 may determine that an intermittent network issues have occurred anda sustained node outage will not occur, indicated by a dash-dottedcross. For the second network node device 120, the proactive networkdiagnosis module 132 may determine that a sustained node outage willoccur indicated by a dotted cross.

In some embodiments, the proactive network diagnosis module 132 maycommunicate with the service server 140 to take actions based on wherethe intermittent network issues have occurred for a specific node or asustained node outage may occur. For example, the proactive networkdiagnosis module 132 may instruct the service server 140 to allocateresources based on data associated with the specific node and maydispatch a technician to locations associated with the specific node. Insome embodiments, the proactive network diagnosis module 132 may sendinformation associated with the issues and the node in near real-time orreal-time to the service server 140. For example, the proactive networkdiagnosis module 132 may send a point of failure for the issues andassociated data to the service server 140. As another example, theproactive network diagnosis module 132 may generate a graphicalrepresentation of a network topology overlaid with the points offailure. In some embodiments, the proactive network diagnosis module 132may be included in the headend device 104. In some embodiments, theproactive network diagnosis module 132 may perform a polling processdescribed in FIGS. 2A-2B.

FIGS. 2A-2B depict an example device polling process for a network 200,in accordance with one or more example embodiments of the disclosure.The example network 200 depicted in FIGS. 2A and 2B may be a DOCSISnetwork, however, it should be noted that this is merely forexemplification purposes, and the systems and methods described hereinmay similarly apply to any other type of network, including any othertypes of devices as well. The network 200 may include one or morenetwork node devices 202 (e.g., one embodiment of the network nodedevices 108 and 120), one or more coaxial splitters 204, one or moreamplifiers 206 (e.g., one embodiment of the amplifiers 110 a, 110 b, 122a, and 122 b), one or more taps (e.g., tap 208, tap 208 b, tap 208 c, orany other number of taps) (for simplicity, reference may be made hereinto “tap 208,” which may represent any of the depicted taps) (e.g., oneembodiment of the taps 112 a, 112 b, 124 a, and 124 b), one or morepremises (e.g., premises 210 a, . . . , premises 210 g, or any othernumber of premises) (for simplicity, reference may be made herein to“premises 210,” which may represent any of the depicted premises), andone or more CPEs (for example, CPE 212 a, . . . , CPE 212 i, or anyother number of CPEs) (for simplicity, reference may be made herein to“CPE 212,” which may represent any of the depicted CPEs) (e.g., oneembodiment of CPEs 116 a, 116 b, 128 a, and 128 b). In some instances, apremises 210 may be a residential home or a commercial building. A CPE212 may be a CPE or any other type of device that may be located in acustomer's home or commercial building, such as a modem, for example.The example network 200 and its potential architectures may be describedin more detail with respect to FIGS. 4 and 5.

In some embodiments, the polling process may begin by taking arepresentative sample of telemetry data from one or more of the mostdownstream devices in the network 200 (for example, the CPEs). Therepresentative sample may include retrieving telemetry data from asubset of CPEs connected to each premises. For example, telemetry datamay be retrieved for CPEs 212 a, 212 c, 212 d, 212 f, 212 g, and 212 h.The telemetry data may include, for example, identifying information forthe device itself, such as the MAC address, device status, device DOCSISprofile, channel frequency, modulation profile, modulation type, andchannel bandwidth, for example. The information may be provided bychannel for each device. The telemetry data may also include performancedata associated with the device, such as forward error correctioninformation, upstream/downstream data transfer rates, signal to noiseratios, device power levels, cable modem termination system (CTMS) powerlevels, CMTS Equalization Coefficient, and device EqualizationCoefficient. By retrieving telemetry data from each of these CPEs, arepresentative sampling of telemetry data for each premises on thenetwork 200 may be quickly retrieved. In some instances, the CPEs thatare polled may be changed through each polling iteration (for example,CPEs 212 b, 212 c, 212 e, 212 f, 212 g, and 212 i may be polled in asubsequent iteration), however, in other instances, the CPEs that arepolled may remain the same.

In some embodiments, once the representative sample is obtained from theCPEs, it may be determined if any of the CPEs in the representativesample are experiencing an impairment. This determination may be madebased on detected micro-reflections. The micro reflections may be a wayof determining how much equalization is occurring on a network. Sincepre-equalization may be a method for overcoming noise in a network, itmay be assumed that high micro reflections are indicative of networkimpairments.

Once it is determined that a CPE on the network 200 is experiencing animpairment, the full network mapping, as described above, may beperformed. In the example provided in FIG. 2A, the CPE 212 g isdetermined to be experiencing an impairment (for example, as indicateswith the cross through CPE 212 g). The full network mapping may beginwith polling (for example, retrieving telemetry data, as describedabove) from devices upstream from the CPE 212 g until an upstream devicewith additional downstream devices is reached (for example, the tap 208c). Once this upstream device is reached, polling may be performed forboth neighboring upstream devices (for example, tap 208 b), as well asany other downstream device connected to the upstream device. Thisprocess may continue until all devices on the network 200 have beenpolled (or, in some cases, only a larger subset of devices on thenetwork 200). This process may be illustrated in FIG. 2B. For instance,starting from tap 208 c, premises 210 g may be polled, and then the twoCPEs 212 h and 212 i connected to the premises 210 g may subsequently bepolled. The retrieval of telemetry data may thus spread “outwards” fromthe common upstream device until all telemetry data for all devices onthe network 200 has been retrieved. The result of this full networkmapping as depicted in FIG. 2B may be that CPEs 212 a, 212 b, 212 d, 212f, and 212 h are also identified as experiencing an impairment alongwith CPE 212 g. It should be noted that although FIG. 2B only depictsthe mapping as reaching premises 210 d and 210 e, and the devicesdownstream from tap 208 c, this is only for exemplary purposes, and themapping may continue to spread until all of the devices on the network200 are reached. In some embodiments, the above polling process may beperformed by the system 100. The network 200 may be one embodiment ofthe cable network in the system 100.

FIG. 3 depicts an example flow diagram showing a method 300 forproactive network diagnosis, in accordance with one or more exampleembodiments of the present disclosure. In FIG. 3, computer-executableinstructions of one or more module(s) of the system 100, and/or thecable network 200 may be executed to perform the proactive networkdiagnosis.

At block 302 of the method 300 in FIG. 3, the method may includedetermining telemetry data and streaming trap data indicative of a groupof cable modem devices being disconnected from a cable network. Forexample, a headend device (e.g., the headend device 104) of a system(e.g., the system 100 of FIG. 1) may request telemetry data from CPEdevices associated with a tap (e.g., the tap 112 a, 112 b, 124 a, or 124b in FIG. 1, or the tap 208 in FIGS. 2A and 2 b) that is upstream to theCM devices (e.g., the CPE 116 a, 116 b, 128 a, or 128 b in FIG. 1, orthe CPE 212 in FIGS. 2A and 2B) via a polling process described in FIGS.2A and 2B. The headend device may capture the streaming trap data via anSNMP communication. The system 100 may select, based on the telemetrydata and streaming trap data, one or more CM devices of the cablenetwork of the system 100 or the cable network 200 that have beenexperiencing or experienced intermittent network issues (e.g.,intermittently off) and/or sustained offline conditions (e.g.,completely off).

At block 304, the method may include determining, based on the telemetrydata and the streaming trap data, a first network node device, and afirst CM device and a second CM device of the group of CM devices areassociated with the first network node device. For example, the systemmay attribute the selected CM devices to a specific node based on thetelemetry data and the streaming trap data. As shown in FIG. 1, the CPEs116 b, 128 a, and 128 b may be selected. The CPE 116 b may be attributedto the first network node device 108. The CPE 128 a, and 128 b may beattributed to the second network node device 120.

At block 306, the method may include determining, first performance dataassociated with the first network node device. For example, in FIG. 1,the system 100 may determine performance data associated with the firstnetwork node device 108 or the second network node device 120.Performance data may include forward error correction information,upstream/downstream data transfer rates, signal to noise ratios (SNRs),modulation error ratios, measurement data to characterize one or morechannels associated with signals outputted by CPEs (e.g., slopesmeasurement data, amplitude measurement data, ripple measurement data,spike (for example, non-linear noise distortions) measurement data,absolute received power per subcarrier measurement data, error vectormagnitude measurement data, and the like), device power levels, CMTSpower levels, a CMTS equalization coefficient, a device equalizationcoefficient, and/or any relevant data associated with a performance ofthe above devices.

At block 308, the method may include determining, based on the firstperformance data, a pattern associated with the first network nodedevice. In some embodiments, a pattern may indicate a distribution(e.g., a statistical distribution, or the like) of the various types ofperformance data. In some embodiments, a pattern may indicate a changeof particular performance data (e.g., a signal to noise ratio, or anyrelevant performance data) over a time period (e.g., a function of theperformance data versus time). In some embodiments, a pattern mayindicate a distribution of CM devices (e.g., CPEs having intermittentlyoff and/or completely off) associated with a specific node. A nodehaving been experiencing an intermittent network issue/havingexperienced an intermittent network issue/having a sustained node outagemay be associated with a specific pattern.

At block 310, the method may include determining, based on a machinelearning model, a value indicative of a probability of the occurrence ofthe event. For example, the system may generate a machine learningmodel. The system may train the machine learning model using historicaldata (e.g., known patterns, historical performance data), and associatedintermittent network issues and sustained node outage. The system mayinput a detected pattern or measured performance data into the trainedmachine learning model and the trained machine learning model may outputa value indicative of a probability of the occurrence of the event thatdescribes a likelihood indicative of how likely an event may occur(e.g., how likely the intermittent network issues have occurred or theintermittent network issues have been occurring, and/or how likely thesustained node outage will occur). In some embodiments, the system mayuse the historical data to train a machine learning model to classify anode into different classifications (e.g., a classification indicativeof nodes having been experiencing intermittent network issues, aclassification indicative of nodes having experienced intermittentnetwork issues, a classification indicative of nodes will experience asustained node outage, a classification indicative of a bad node that iscompletely off, a classification indicative of a healthy node, or thelike). The system may input a detected pattern or measured performancedata into the trained machine learning model, and the trained machinelearning model may output a likelihood indicative of a classificationassociated with a specific node.

At block 312, the method may include determining, based on a comparisonbetween the first performance data and an event criterion, an occurrenceof an event associated with the first node device. In some embodiments,the system may compare the first performance data with one or morecontrol thresholds. If the first performance data deviates from the oneor more control thresholds, the system may determine an occurrence of anevent. If the first performance data does not deviate from one or morecontrol thresholds, the system may determine that the event is notoccurring, has not occurred, or will not occur. In some embodiments, thesystem may compare a detected pattern with a historical pattern. If thedetected pattern match the historical pattern, the system may determinean occurrence of an event. If the detected pattern does not match thehistorical pattern, the system may determine that the event is notoccurring, has not occurred, or will not occur. In some embodiments, thesystem may input the first performance data and/or detected pattern intoa machine learning model to determine a value indicative of aprobability of the occurrence of the event (e.g., a likelihoodindicative of how likely an event may occur, or a likelihood indicativeof a classification associated with a specific node). The system maycompare the value with one or more probability thresholds. A probabilitythreshold may describe a value or a value range indicative of whether ornot the event may occur or of whether or not a classification thespecific node may belong to. If the value deviates from a probabilitythreshold, the system may determine the occurrence of the event. If thevalue does not deviate from the probability threshold, the system maydetermine that the event is not occurring, has not occurred, or will notoccur. In some embodiments, the system may compare the value withmultiple probability thresholds. The multiple probability thresholds maydescribe a value range indicative of whether or not the event may occuror of whether or not a classification the specific node may belong to.For example, if the value falls within a particular value range definedby a first probability threshold and a second probability threshold(e.g., the particular value range may be associated with a particularclassification), the system may determine a particular classificationthe specific node may belong to.

FIG. 4 shows an example diagram 400 of a portion of a cable network andassociated devices in a particular network deployment, in accordancewith example embodiments of the disclosure. In some instances, the cablenetwork depicted in the example diagram 400 may be similar to the cablenetwork of the system 100 and/or network 200, and may provide additionaldetails regarding the potential network architecture of such networksdescribed herein. In some aspects, the cable network described hereinmay be implemented using a DOCSIS specification. The device 404 mayinclude a CMTS, which can also be referred to as an access controller, acontroller, and/or a node herein. In an embodiment, the device 404 mayhave a converged cable access platform (CCAP) functionality. In anotherembodiment, the device 404 may serve as a remote physical (PHY) device,that is, a device having PHY layer functionality (that is, PHY layerfunctionality as described in connection with the open systemsinterconnection model, OSI model).

In an embodiment, there can be a fiber 414 connected to the device 404.The device 404 can further be connected to various network cable taps406, 408, and 410, also referred to as taps or terminations herein, andcan connect to various cable CPEs (e.g., CM devices), for example, atvarious households 412.

In some embodiments, a cable network can include a fiber optic network,which can extend from the cable operators' headend out to aneighborhood's hub site, and finally to a coaxial cable node whichserves customers, for example, 25 to 2000 households (or any number ofother households, or even commercial buildings).

In an embodiment, data can be transmitted downstream from the device 404to one or more homes 412 over drop cables 416 (also referred to as dropsherein) using one or more taps 406, 408, and 410. In an embodiment, asthe data is transmitted downstream from the device 404 to one or morehomes 412, the taps 406, 408, and 410 can potentially generate variousimpairments on the network. Alternatively, or additionally, as thesignals pass through from the device 404 to the taps 406, 408, and 410over fibers 414 and to the homes 412 over one or more drops 416, thefibers 414 and/or the drops 416 can cause the signals to undergo variousimpairments, for example, to the power spectral density of the signals.In an embodiment, the impairment can be due to attenuation on the fibers414 and/or drops 416. In an embodiment, the impairments can lead tofrequency distortions on the signals; for example, the higher frequencyend of the signals may be attenuated. Accordingly, in an embodiment, oneor more amplifiers (not shown) can be used to perform a gain on theattenuated signals. In an embodiment, the one or more amplifiers can beplaced, for example, at one or more of the taps 406, 408, and 410 toperform the gain on the attenuated signals.

In an embodiment, the homes 412, the devices in the homes 412, and taps406, 408, and/or 410 can introduce different distortions on the dropcables 416 and/or fibers 414. In an embodiment if the distortion isintroduced on a given fiber 414 feeding a first tap 406 of the taps 406,408, and/or 410, different homes of the homes 412 may receive a similardistortion to signals being transmitted and received from one or moredevices at the homes 412. In another embodiment, a distortion in a giventap of the taps 406, 408, and/or 410, a distortion at a given drop ofthe drops 416, or distortions associated with one or more cables and/orwires of one or more devices in a given home of the homes 412, may causesignals being received and transmitted at the various taps 406, 408,and/or 410, and/or signal being transmitted or received by the devicesin the different homes 412 to undergo different signal distortions.

Likewise, in an embodiment, if the devices at various homes 412 aretransmitting data upstream, the distortion to the signals experienced bydevices at different homes 412 can be different. In an embodiment, thedisclosed systems, methods, and apparatuses describe techniques by whichvarious devices, for example, the various devices in the homes 412 mayneed to transmit to account for the different distortions on thenetwork, as described above.

In an embodiment, a given transmitting device on the network cantransmit a pre-determined sequence, for example a 32-symbol (or anysuitable number of symbol) sequence where each symbol includes apre-determined amount of data, to the receiving devices. Accordingly,when the receiving devices receive the pre-determined sequence, thereceiving devices may be programmed to be able to determine one or morecharacteristics that the signal associated with the receivedpre-determined sequence should have. Therefore, the receiving device candetermine whether there is a deviation from what the receiving devicewould have received absent distortions in the received signal.

In an embodiment, the receiving devices can take various measurements,for example, measurements to characterize one or more channelsassociated with signals received by the receiving device, and/or one ormore measurements associated with the received signal from thetransmitting device, including, but not limited to, signal-to-noiseratio (SNR) measurements, modulation error ratios (MER) measurements,slopes measurements, amplitude measurements, ripple measurements, spike(for example, non-linear noise distortions) measurements, absolutereceived power per subcarrier measurements, error vector magnitudemeasurements, and the like. The receiving device can then transmit atleast portions of the measurements in informational blocks, for example,as data packets, back to the transmitting device. The transmittingdevice can modify a table based on the received information from thereceiving device, for example, a table stored in internal memory. In anembodiment, the table can include information such as what sequence thereceiving device would have received absent any distortions to thesignal, what sequence the receiving device actually received, whatsequence the transmitting device received back from the receiving device(which may also be subject to further distortions), and/or anyinformation indicative of the measurements taken by the receiving deviceof the channel and/or the received signal, as described above. In someembodiments, the CMTS or controller can determine interference groups(IGs) based on the received information from the receiving devices.

In some aspects, the cable network, as described above, can include aregional or area headend/hub. The hub can receive programming that isencoded, modulated and upconverted onto radio frequency (RF) carriers,combined onto a single electrical signal and inserted into a broadbandtransmitter, for example, an optical transmitter. In some embodiments,the transmitter can convert the electrical signal to a downstreammodulated signal that is sent to the nodes. Fiber optic cables connectthe headend or hub to nodes in various topologies, including, but notlimited to, point-to-point or star topologies, ring topologies, and thelike.

By using frequency-division multiplexing, an HFC network such as the onedescribed in connection with FIG. 4 may carry a variety of services, forexample, analog and digital TV, video on demand, telephony, data, andthe like. Services on these systems can be carried on radio frequency(RF) signals in particular regions of the available spectrum.

The HFC network can be operated bi-directionally such that signals arecarried in both directions on the same network. For example, the signalscan be transmitted from the headend/hub office to the customer, and fromthe customer to the headend/hub office. In one aspect, a forward-path ordownstream signals can refer to information from the headend/hub officeto the customer. In another aspect, a return-path or upstream signalscan refer to information from the customer to the headend/hub office.The forward-path and the return-path can be carried over the samecoaxial cable in both directions between the node and the customerequipment.

In some embodiments, HFC network can be structured to be asymmetrical,that is, data transmission in the downstream direction has much moredata-carrying capacity than the upstream direction. Full Duplex canrefer to a network specification that can improve upon DOCSIS 3.1 to usethe full spectrum of the cable plant (for example, from approximately 0MHz to approximately 1.2 GHz) at the same time in both upstream anddownstream directions. This technology may facilitate multi-gigabitsymmetrical services while remaining backwards compatible with DOCSIS3.1.

In some embodiments, the DOCSIS may include at least one of (i) a DOCSIS1.0, (ii) a DOCSIS 2.0, (iii) a DOCSIS 3.0, (iv) a DOCSIS 3.1, or (v) aDOCSIS 4.0 specification.

FIG. 5 shows example diagrams of another network architecture, inaccordance with example embodiments of the disclosure. The networkarchitecture may be similar to the cable network of the system 100and/or network 200, and may provide additional details regarding thepotential network architecture of such networks described herein. Thatis, the network of the system 100 and the network 200 may includeelements of the network architectures depicted in FIGS. 4 and 5. In oneembodiment, as shown in diagram 501, device 504 can represent a headenddevice. In one aspect, device 504 can include converged cable accessplatform (CCAP) device. CCAP can refer to equipment that combinesaspects of the functionality of edge quadrature amplitude modulation(QAM) technology with cable model termination systems (CMTSs) to provideservices such as internet and voice over IP while encoding andtransmitting digital video channels over the cable network.

In one embodiment, the device 504 can be electronically connected todevice 508, which can represent a network node device, for example, anetwork node device such as remote physical (PHY) device, remote mediumaccess control (MAC) device, and/or remote hybrid PHY/MAC device. In oneaspect, the electronic connection between device 504 and device 508 canbe via a cable 506, for example, a fiber optic cable. In an embodiment,device 504 and/or 508 can encompass aspects of the functionality of acomputing entity 600, described below. In one embodiment, one or more ofthe devices 504 and/or 508 can include a switch, for example, a networkswitch such as an Ethernet switch.

In some embodiments, as shown in diagram 501, the device 508 can beelectronically connected to a customer premise, for example, a home 516(also referred to herein as a household) and various devices associatedwith the home 516. In another aspect, the device 508 can be connected tothe home 516 through one or more amplifiers 512, and/or one or more taps514. In some respects, the amplifiers can serve to amplify signals torestore attenuation of the signals during propagation over the network.In another aspect, diagram 501 can represent a node-x embodiment, wherenode-x can represent a node having a variable number of amplifiers at agiven node split. In particular, in diagram 501, the network can havefive amplifiers in each of the four node splits.

Similarly, as shown in diagram 503, the device 528 can be electronicallyconnected to a customer premise, for example, a home 536 (also referredto herein as a household) and various devices associated with the home536. In another aspect, the device 528 can be connected to the home 536through one or more amplifiers 532, and/a tap 534. In the case ofdiagram 503, the number of node splits at the device 528 can be lessthan the number of node splits in diagram 501 (e.g., two-node splits vs.four-node splits).

Similarly, as shown in diagram 505, the device 538 can be electronicallyconnected to a customer premise, for example, a home 548 (also referredto herein as a household) and various devices associated with the home548. In another aspect, the device 538 can be connected to the home 548through one or more amplifiers 542 and a tap 546. In the case of diagram505, the number of node splits at the device 538 can be less than thenumber of node splits in diagram 503 (e.g., zero-node splits vs.two-node splits). Accordingly, diagram 503 can represent a node-zeroarchitecture, which can work without amplifiers.

In some embodiments, the disclosure describes providing the cablenetworks, such as the node-x and node-zero cable network deploymentsdescribed above, with the ability to turn off sounding and resourceblock allocations in a given portion of the spectrum associated with FDXoperation. Further, the disclosure describes configuring that portion ofthe spectrum as being static (i.e., not dynamic) with regards to changesin the starting and ending frequency. In some aspects, the disclosuredescribes simulating a hisplit using FDX.

As mentioned, lowsplit can refer to an approximately 5 MHz toapproximately 42 MHz split in the United States. Midsplit can refer toan approximately 5 MHz to approximately 85 MHz split in the UnitedStates. Hisplit can refer to an approximately 5 MHz to approximately 204MHz split in the United States; however, hisplit may not be generallydeployed. In some embodiments, if amplifiers are used in a network thatmakes use of the node-x, the amplifiers may need to be replaced withdevices having an approximately 5 MHz to approximately 204 MHz (orhigher, up to approximately 684 MHz) return and an approximately 1 GHzto approximately 1.2 GHz forward capability.

In some embodiments, the system represented by diagram 501 (andsimilarly for diagrams 503 and 505, implicitly described in thefollowing) may include a source (not shown) which may be connected todevice 504 via cables 502 and 506. In another aspect, the system caninclude additional devices 504 and 508 and a tap or terminator 514. Thesource may be configured to provide a downstream broadband signal to oneor more customer devices and receive upstream signals from the one ormore customer devices, for example, customer devices at the homes 516.The devices 504 and 508 may be configured (i) to receive the downstreambroadband signal via the cables 502 and 506, (ii) convert the downstreambroadband signal into a radio frequency downstream signal, (iii) outputthe downstream broadband signal onto one or more cable lines (forexample, cables 502 and 506) for communication to the one or morecustomer devices, (iv) receive the upstream signals via the one or morecable lines, and (v) convert the received upstream signals into lightsignals for communication to the source via the cables 502 and 506. Theterminator 514 may be in communication with the devices 504 and 508 viathe cables, and the terminator 514 may be configured to output the radiofrequency downstream signal for receipt by the one or more customerdevices and direct the communication of the upstream signals to theoptical fiber node via the one or more cables.

In some embodiments, the source may be a suitable source of broadbandcontent, such as a cable plant. The source may be configured to generateand/or combine any number of data streams and/or data components into abroadband signal that is output by the source for receipt by one or morehouseholds, for example, homes 516. For example, the source may beconfigured to obtain video data streams from one or more contentproviders, such as television networks, premium content providers,and/or other content providers, and the source may be configured togenerate a broadband signal based at least in part on the video datastreams. As desired, the source may insert commercials and/or other datainto a television or video component of a broadband signal.Additionally, the source may be configured to generate or obtain anynumber of data components that are inserted or added to a broadbandsignal, such as television guide data, an Internet data signal, homesecurity data signals, voice over internet protocol (VoIP) telephonesignals, etc. Any number of modulation techniques and/or data standardsmay be utilized by a source in the generation or compilation of abroadband data signal. For example, television data may be modulatedutilizing a suitable quadrature amplitude modulation (QAM) or othermodulation technique, and the modulated data may be incorporated intothe broadband data signal. As another example, an orthogonalfrequency-division multiple access (OFDMA) technique, a time divisionmultiple access (TDMA) technique, an advanced time division multipleaccess (ATDMA) technique, a synchronous code division multiple access(SCDMA) technique, or another suitable modulation technique or schememay be utilized to modulate data included within the broadband datasignal. The broadband data signal may be configured to provide a widevariety of services to one or more households, including but not limitedto, television service, telephone service, Internet service, homemonitoring service, security service, etc.

In certain embodiments, the generated broadband signal may be outpututilizing one or more cables 502 and/or 506, for example, fiber opticcables or optical fibers that are configured to carry the broadbandsignal from the source to one or more corresponding devices, forexample, devices 504 and 508. For example, the radio frequency broadbandsignal may be processed utilizing one or more suitablewavelength-division multiplexing (WDM) devices or WDM systems, and theprocessed signal may be provided to or driven onto an optical fiber. Awide variety of different types of WDM devices may be utilized asdesired in various embodiments of the disclosure, such as dense WDMdevices and add-drop WDM devices. As desired, a WDM device may include aterminal multiplexer component that includes one or more wavelengthconverting transponders. Each wavelength converting transponder mayreceive one or more components of the input broadband signal and convertthat signal into a light signal using a suitable laser, such as a 1550nm band laser. The terminal multiplex may also contain an opticalmultiplexer configured to receive the various 1550 nm band signals andplace or drive those signals onto a single optical fiber.

As desired, the WDM device may amplify the broadband signals that areprocessed by the WDM device. Additionally or alternatively, one or moreline repeaters or other amplifying devices (such as amplifiers 512) maybe positioned along a length of the optical fiber in order to amplifythe broadband signal and compensate for any losses in optical power.

In addition to processing downstream or forward-path signals that arereceived from the source, the WDM device may be configured to receiveand process upstream signals that are communicated to the source fromthe households, for example, from homes 516. Cables, for example cables502 and 506 (which can include, for example, optical fibers) may beconfigured to carry broadband signals between the source the devices 504and 508, and the taps 514. These signals may include forward pathsignals generated by the source and return path signals generated by oneor more households, for example, from homes 516. A wide variety ofdifferent optical fibers may be utilized as desired in variousembodiments of the disclosure, such as multi-mode fibers, single-modefibers, and special purpose fibers. Additionally, the optical fibers maybe constructed from a wide variety of different materials, such assilica, fluorides, phosphates, and/or chalcogenides. The optical fibersmay be configured to carry signals as light pulses utilizing totalinternal reflection.

Moreover, any number of devices 504 and 508, which can alternatively oradditionally be referred to as fiber nodes may be provided. Each fibernode may be configured to receive and process downstream or forward pathsignals from the source. Additionally, each fiber node may be configuredto receive and process upstream or return path signals received from theone or more households.

In some embodiments, once a signal has been filtered out or otherwiseisolated by the amplifiers 512, the amplifiers 512 may amplify thesignal. For example, the amplifiers 512 may increase the amplitude ofthe signal. In certain embodiments, the various components of abroadband signal (e.g., low return path, forward path, high return path)may be amplified by respective amplification components of theamplifiers 512. Each amplified signal may then be output onto or drivenback onto the cable line in a desired direction for the signal. Asdesired, any number of diodes or other suitable devices may beincorporated into the amplifiers 512 in order to prevent or limitundesired leakage of an amplified signal in a direction from which thesignal was received. For example, the amplifiers 512 may receive areturn path signal from a terminator 514 or other amplifier, theamplifiers 512 may amplify the signal, and the amplifier may output thesignal in an upstream direction towards devices 504 and 508 and/orsource while limiting the output or leakage of the signal in adownstream direction.

The amplifiers 512 may include a wide variety of gains as desired invarious embodiments of the disclosure. Additionally, as desired,different gains may be utilized for different components of a broadbandsignal. In certain embodiments, the amplifiers 512 may be powered by areceived broadband signal, such as a received downstream signal.Additionally or alternatively, the amplifiers 512 may be powered by oneor more batteries and/or external power sources. In certain embodiments,the power requirements of the amplifiers 512 may be based at least inpart on the modulation technique(s) utilized in association with thebroadband signals that are amplified. In one example embodiment, arelatively low power amplifier may be provided in association with anOFDMA modulation technique.

Any number of terminators 514 or taps may be connected to a cable line.A terminator 514 may form an access point from which one or morehouseholds, such as households 516, may be provided with broadbandservices. Any number of households may be serviced by a terminator 514as desired in various embodiments of the disclosure. For example, incertain embodiments, up to four households may be serviced by aterminator 514. As desired, a cable drop or other signal line (e.g., acoaxial cable or RF cable) may extend from the terminator 7514 to ahousehold 516. In this regard, signals may be provided to and/orreceived from the household 516.

In an embodiment, the signals transmitted between the CTMS and the CMscan be purified via echo cancellation both in the analog and digitaldomain. In another embodiment, an analog echo canceller can reduce theechoes of the signal in the analog domain. In an embodiment the analogecho canceller can reduce the echo, group delay, noise amplitude, andthe like, of the signal. The signal can, alternatively or additionally,proceed to an analog-to-digital converter (ADC) for conversion to thedigital domain. In an embodiment, the signal can proceed thereafter to adigital echo canceller, which can remove echoes and the like in thedigital domain. In an embodiment, the output of the digital echocanceler can be transmitted from the CMTS to a device, for example, acable CPE.

FIG. 6 provides a schematic of a network computing entity 600 accordingto one embodiment of the present disclosure. The computing entity may beused to perform any of the operations described herein, such as theproactive network diagnosis and/or polling process described withrespect to any of FIGS. 1-3, for example. The network computing entity600 may be one embodiment of the remote computing server 130. Ingeneral, the terms computing entity, computer, entity, device, system,and/or similar words used herein interchangeably may refer to, forexample, one or more computers, computing entities, desktop computers,mobile phones, tablets, phablets, notebooks, laptops, distributedsystems, gaming consoles (e.g., Xbox, Play Station, Wii), watches,glasses, iBeacons, proximity beacons, key fobs, radio frequencyidentification (RFID) tags, ear pieces, scanners, televisions, dongles,cameras, wristbands, wearable items/devices, kiosks, input terminals,servers or server networks, blades, gateways, switches, processingdevices, processing entities, set-top boxes, relays, routers, networkaccess points, base stations, and the like, and/or any combination ofdevices or entities adapted to perform the functions, operations, and/orprocesses described herein. Such functions, operations, and/or processesmay include, for example, transmitting, receiving, operating on,processing, displaying, storing, determining, creating/generating,monitoring, evaluating, comparing, and/or similar terms used hereininterchangeably. In one embodiment, these functions, operations, and/orprocesses can be performed on data, content, information, and/or similarterms used herein interchangeably.

As indicated, in one embodiment, the network computing entity 600 mayalso include one or more communications interface(s) 620 forcommunicating with various computing entities, such as by communicatingdata, content, information, and/or similar terms used hereininterchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. For instance, the networkcomputing entity 600 may communicate with user devices 630 (e.g., mobilephone, laptop, computer or the like) and/or a variety of other computingentities.

As shown in FIG. 6, in one embodiment, the network computing entity 600may include or be in communication with one or more processingelement(s) 605 (also referred to as processors, processing circuitry,and/or similar terms used herein interchangeably) that communicate withother elements within the network computing entity 600 via a bus, forexample. As will be understood, the processing element 605 may beembodied in a number of different ways. For example, the processingelement 605 may be embodied as one or more complex programmable logicdevices (CPLDs), microprocessors, multi-core processors, coprocessingentities, application-specific instruction set processors (ASIPs),microcontrollers, and/or controllers. Further, the processing element605 may be embodied as one or more other processing devices orcircuitry. The term circuitry may refer to an entirely hardwareembodiment or a combination of hardware and computer program products.Thus, the processing element 605 may be embodied as integrated circuits,application specific integrated circuits (ASICs), field-programmablegate arrays (FPGAs), programmable logic arrays (PLAs), hardwareaccelerators, other circuitry, and/or the like. As will, therefore, beunderstood, the processing element 605 may be configured for aparticular use or configured to execute instructions stored in volatileor non-volatile media or otherwise accessible to the processing element605. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 605 may becapable of performing steps or operations according to embodiments ofthe present disclosure when configured accordingly.

In one embodiment, the network computing entity 600 may further includeor be in communication with non-volatile media (also referred to asnon-volatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In one embodiment, thenon-volatile storage or memory may include one or more non-volatilestorage or memory media 610, including but not limited to hard disks,ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, MemorySticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipedememory, racetrack memory, and/or the like. As will be recognized, thenon-volatile storage or memory media may store databases, databaseinstances, database management systems, data, applications, programs,program modules, scripts, source code, object code, byte code, compiledcode, interpreted code, machine code, executable instructions, and/orthe like. The term database, database instance, database managementsystem, and/or similar terms used herein interchangeably may refer to acollection of records or data that is stored in a computer-readablestorage medium using one or more database models, such as a hierarchicaldatabase model, network model, relational model, entity-relationshipmodel, object model, document model, semantic model, graph model, and/orthe like.

In one embodiment, the network computing entity 600 may further includeor be in communication with volatile media (also referred to as volatilestorage, memory, memory storage, memory circuitry and/or similar termsused herein interchangeably). In one embodiment, the volatile storage ormemory may also include one or more volatile storage or memory media615, including but not limited to RAM, DRAM, SRAM, FPM DRAM, EDO DRAM,SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM,RIMM, DIMM, SIMM, VRAM, cache memory, register memory, and/or the like.As will be recognized, the volatile storage or memory media may be usedto store at least portions of the databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the likebeing executed by, for example, the processing element 605. Thus, thedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like may be used to control certainaspects of the operation of the network computing entity 600 with theassistance of the processing element 805 and operating system.

As indicated, in one embodiment, the network computing entity 600 mayalso include one or more communications interfaces 620 for communicatingwith various computing entities, such as by communicating data, content,information, and/or similar terms used herein interchangeably that canbe transmitted, received, operated on, processed, displayed, stored,and/or the like. Such communication may be executed using a wired datatransmission protocol, such as fiber distributed data interface (FDDI),digital subscriber line (DSL), Ethernet, asynchronous transfer mode(ATM), frame relay, data over cable service interface specification(DOCSIS), or any other wired transmission protocol. Similarly, thenetwork computing entity 600 may be configured to communicate viawireless external communication networks using any of a variety ofprotocols, such as general packet radio service (GPRS), Universal MobileTelecommunications System (UMTS), Code Division Multiple Access 2000(CDMA2000), CDMA2000 1× (1×RTT), Wideband Code Division Multiple Access(WCDMA), Time Division-Synchronous Code Division Multiple Access(TD-SCDMA), Long Term Evolution (LTE), Evolved Universal TerrestrialRadio Access Network (E-UTRAN), Evolution-Data Optimized (EVDO), HighSpeed Packet Access (HSPA), High-Speed Downlink Packet Access (HSDPA),IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX), ultra-wideband (UWB),infrared (IR) protocols, near field communication (NFC) protocols,Wibree, Bluetooth protocols, wireless universal serial bus (USB)protocols, and/or any other wireless protocol.

Although not shown, the network computing entity 600 may include or bein communication with one or more input elements, such as a keyboardinput, a mouse input, a touch screen/display input, motion input,movement input, audio input, pointing device input, joystick input,keypad input, and/or the like. The network computing entity 600 may alsoinclude or be in communication with one or more output elements (notshown), such as audio output, video output, screen/display output,motion output, movement output, and/or the like.

As will be appreciated, one or more components of the network computingentities 600 may be located remotely from other network computing entity600 components, such as in a distributed system. Furthermore, one ormore of the components may be combined, and additional componentsperforming functions described herein may be included in the networkcomputing entity 600. Thus, the network computing entity 600 can beadapted to accommodate a variety of needs and circumstances. As will berecognized, these architectures and descriptions are provided forexample purposes only and are not limiting to the various embodiments.

Although an example processing system has been described above,implementations of the subject matter and the functional operationsdescribed herein can be implemented in other types of digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them.

Embodiments of the subject matter and the operations described hereincan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. Embodiments of the subject matter describedherein can be implemented as one or more computer programs, i.e., one ormore modules of computer program instructions, encoded on a computerstorage medium for execution by, or to control the operation of, aninformation/data processing apparatus. Alternatively, or in addition,the program instructions can be encoded on an artificially generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, which is generated to encode information/datafor transmission to a suitable receiver apparatus for execution by aninformation/data processing apparatus. A computer storage medium can be,or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described herein can be implemented as operationsperformed by an information/data processing apparatus oninformation/data stored on one or more computer-readable storage devicesor received from other sources.

The term “data processing apparatus” encompasses all kinds ofapparatuses, devices, and machines for processing data, including by wayof example a programmable processor, a computer, a system on a chip, ormultiple ones, or combinations, of the foregoing. The apparatus caninclude special purpose logic circuitry, e.g., an FPGA (fieldprogrammable gate array) or an ASIC (application specific integratedcircuit). The apparatus can also include, in addition to hardware, codethat creates an execution environment for the computer program inquestion, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them. The apparatus and execution environment canrealize various different computing model infrastructures, such as webservices, distributed computing, and grid computing infrastructures.

A computer program (also known as a program, software, a softwareapplication, a script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages, anddeclarative or procedural languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, object, or other unit suitable for use in a computingenvironment. A computer program may, but need not, correspond to a filein a file system. A program can be stored in a portion of a file thatholds other programs or information/data (e.g., one or more scriptsstored in a markup language document), in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, subprograms, or portions of code). A computerprogram can be deployed to be executed on one computer or on multiplecomputers that are located at one site or distributed across multiplesites and interconnected by a communication network.

The processes and logic flows described herein can be performed by oneor more programmable processors executing one or more computer programsto perform actions by operating on the input of information/data andgenerating output. Processors suitable for the execution of a computerprogram include, by way of example, both general and special purposemicroprocessors, and any one or more processors of any kind of digitalcomputer. Generally, a processor will receive instructions andinformation/data from a read-only memory or a random access memory orboth. The essential elements of a computer are a processor forperforming actions in accordance with instructions and one or morememory devices for storing instructions and data. Generally, a computerwill also include, or be operatively coupled to receive information/datafrom or transfer information/data to, or both, one or more mass storagedevices for storing data, e.g., magnetic, magneto-optical disks, oroptical disks. However, a computer need not have such devices. Devicessuitable for storing computer program instructions and information/datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described herein can be implemented on a computer having adisplay device, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information/data to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user, for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described herein can be implemented ina computing system that includes a back-end component, e.g., as aninformation/data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a web browserthrough which a user can interact with an implementation of the subjectmatter described herein, or any combination of one or more suchback-end, middleware, or front-end components. The components of thesystem can be interconnected by any form or medium of digitalinformation/data communication, e.g., a communication network. Examplesof communication networks include a local area network (LAN) and a widearea network (WAN), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of the client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits information/data (e.g., an HTML page) toa client device (e.g., for purposes of displaying information/data toand receiving user input from a user interacting with the clientdevice). Information/data generated at the client device (e.g., a resultof the user interaction) can be received from the client device at theserver.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyembodiment or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments. Certain features that aredescribed herein in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

Many modifications and other embodiments of the disclosure set forthherein will come to mind to one skilled in the art to which theseembodiments pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the embodiments are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A system comprising: at least one processor; and at least one memorystoring computer-executable instructions, that when executed by the atleast one processor, cause the at least one processor to: determinetelemetry data associated with a group of cable modem devices; determinestreaming trap data indicative of the group of cable modem devices beingdisconnected from a cable network, wherein the telemetry data and thestreaming trap data are received from a cable modem termination systemthat communicates with the group of cable modem devices via a group ofnetwork node devices; determine, based on the telemetry data andstreaming trap data, a first network node device of the group of networknode devices, wherein a first cable modem device and a second cablemodem device of the group of cable modem devices are associated with thefirst network node device; generate first performance data associatedwith the first network node device; determine, based on a comparisonbetween the first performance data and an event criterion, an occurrenceof an intermittent event associated with the first network node device;and determine, based on a machine learning model and the occurrence ofthe intermittent event, a value indicative of a probability of a futureoccurrence of a sustained event.
 2. The system of claim 1, wherein thecomputer-executable instructions further cause the at least oneprocessor to: determine, based on the telemetry data and the streamingtrap data, a second network node device of the group of network nodedevices, wherein a third cable modem device of the group of cable modemdevices is associated with the second network node device; determinesecond performance data associated with the second network node device;and determine, based on a comparison between the second performance dataand the event criterion, an occurrence of an event associated with thesecond network node device.
 3. The system of claim 1, wherein thecomputer-executable instructions further cause the at least oneprocessor to: requesting, by the cable modem termination system of thesystem, telemetry data from a first tap device that is upstream to thefirst cable modem device and a third cable modem device of the group ofcable modem devices; determine that the first cable modem device isdisconnected from the cable network; determine that a second tap deviceis located laterally to the first tap device; request telemetry datafrom the second tap device that is upstream to the second cable modemdevice; determine that the second cable modem device is disconnectedfrom the cable network; and determine the group of cable modem devicesincluding the first cable modem device and the second cable modemdevice.
 4. The system of claim 1, wherein the event criterion comprisesone or more control thresholds, one or more probability thresholds, andone or more historical data.
 5. The system of claim 1, wherein thecomputer-executable instructions further cause the at least oneprocessor to: determine that the value deviates from a probabilitythreshold; and wherein determining that the occurrence of the sustainedevent is based on that the value deviates from the probabilitythreshold.
 6. The system of claim 1, wherein the computer-executableinstructions further cause the at least one processor to: determine,based on the first performance data, a pattern associated with the firstnetwork node device, wherein determining the occurrence of theintermittent event or the sustained event is based on the pattern. 7.The system of claim 1, further comprising allocating one or moreresources to locations associated with the first network node device. 8.A method comprising: determining, by one or more processors, telemetrydata associated with a group of network node devices; determining,streaming trap data indicative of the group of cable modem devices beingdisconnected from a cable network, wherein the telemetry data and thestreaming trap data are received from a cable modem termination systemthat communicates with the group of cable modem devices via a group ofnetwork node devices; determining, based on the telemetry data and thestreaming trap data, a first network node device of the group of networknode devices, wherein a first cable modem device and a second cablemodem device of the group of cable modem devices are associated with thefirst network node device; generating first performance data associatedwith the first network node device; and determining, based on acomparison between the first performance data and an event criterion, anoccurrence of an intermittent event associated with the first networknode device; and determining, based on a machine learning model and theoccurrence of the intermittent event, a value indicative of aprobability of a future occurrence of a sustained event.
 9. The methodof claim 8, further comprising: determining, based on the telemetry dataand the streaming trap data, a second network node device of the groupof network node devices, wherein a third cable modem device of the groupof cable modem devices is associated with the second network nodedevice; determining second performance data associated with the secondnetwork node device; and determining, based on a comparison between thesecond performance data and the event criterion, an occurrence of anevent associated with the second network node device.
 10. The method ofclaim 8, further comprising: requesting, by the cable modem terminationsystem of the system, telemetry data from a first tap device that isupstream to the first cable modem device and a third cable modem deviceof the group of cable modem devices; determining that the first cablemodem device is disconnected from the cable network; determining that asecond tap device is located laterally to the first tap device;requesting telemetry data from the second tap device that is upstream tothe second cable modem device; determining that the second cable modemdevice is disconnected from the cable network; and determining the groupof cable modem devices including the first cable modem device and thesecond cable modem device.
 11. The method of claim 8, wherein the eventcriterion comprises one or more control thresholds, one or moreprobability thresholds, and one or more historical data.
 12. The methodof claim 8, further comprising: determining that the value deviates froma probability threshold; and wherein determining that the occurrence ofthe sustained event is based on that the value deviates from theprobability threshold.
 13. The method of claim 8, further comprising:determining, based on the first performance data, a pattern associatedwith the first network node device, wherein determining the occurrenceof the intermittent event or the sustained event is based on thepattern.
 14. The method of claim 8, further comprising allocating one ormore resources to locations associated with the first network nodedevice.
 15. A non-transitory computer readable medium includingcomputer-executable instructions stored thereon, which when executed byone or more processors of a wireless access point, cause the one or moreprocessors to perform operations of: determining, by one or moreprocessors, telemetry data associated with a group of network nodedevices; determining streaming trap data indicative of the group ofcable modem devices being disconnected from a cable network, wherein thetelemetry data and the streaming trap data are received from a cablemodem termination system that communicates with the group of cable modemdevices via a group of network node devices; determining, based on thetelemetry data and the streaming trap data, a first network node deviceof the group of network node devices, wherein a first cable modem deviceand a second cable modem device of the group of cable modem devices areassociated with the first network node device; generating firstperformance data associated with the first network node device; anddetermining, based on a comparison between the first performance dataand an event criterion, an occurrence of an intermittent eventassociated with the first network node device; and determining, based ona machine learning model and the occurrence of the intermittent event, avalue indicative of a probability of a future occurrence of a sustainedevent.
 16. The non-transitory computer readable medium of claim 15,further comprising: determining, based on the telemetry data and thestreaming trap data, a second network node device of the group ofnetwork node devices, wherein a third cable modem device of the group ofcable modem devices is associated with the second network node device;determining second performance data associated with the second networknode device; and determining, based on a comparison between the secondperformance data and the event criterion, an occurrence of an eventassociated with the second network node device.
 17. The non-transitorycomputer readable medium of claim 15, further comprising: requesting, bythe cable modem termination system of the system, telemetry data from afirst tap device that is upstream to the first cable modem device and athird cable modem device of the group of cable modem devices;determining that the first cable modem device is disconnected from thecable network; determining that a second tap device is located laterallyto the first tap device; requesting telemetry data from the second tapdevice that is upstream to the second cable modem device; determiningthat the second cable modem device is disconnected from the cablenetwork; and determining the group of cable modem devices including thefirst cable modem device and the second cable modem device.
 18. Thenon-transitory computer readable medium of claim 15, further comprising:determining that the value deviates from a probability threshold; andwherein determining that the occurrence of the sustained event is basedon that the value deviates from the probability threshold.
 19. Thenon-transitory computer readable medium of claim 15, further comprising:determining, based on the first performance data, a pattern associatedwith the first network node device, wherein determining the occurrenceof the intermittent event or the sustained event is based on thepattern.
 20. (canceled)
 21. The system of claim 1, wherein theintermittent event is an impairment, and wherein the intermittent eventis based on a detection of a micro-reflection.